Abstract The Acoustic Emission (AE) phenomenon has been used as a powerful tool with the purpose to either detect, locate or assess damage for a wide range of applications. Derived from its monitoring, one major current challenge on the analysis of the acquired signal is the proper identification and separation of each AE event. Current advanced methods for detecting events are primarily focused on identifying with high accuracy the beginning of the AE wave; however, the detection of the conclusion has been disregarded in the literature. For an automatic continuous detection of events within a data stream, this lack of accuracy for the conclusion of the events generates errors in two critical aspects. In one hand, it deteriorates the accuracy of the measurement of the events duration, truncating the span of the event, which is undesirable in evaluation applications; and in the other hand, it causes false detections. In this work, an accurate and computationally efficient AE activity detector is presented, using a framework inspired by the area of speech processing, and which provides the required indicators to accurately detect the onset and the end of an AE event. This is achieved by means of a threshold approach that instead of directly operates with the transduced voltage signal it does so over the Short-Term Energy and the Short-Term Zero-Crossing Rate measures of the signal. The STE-ZCR method is developed for an application related to the continuous monitoring of a single AE channel derived from the characterization of metallic components by means of a uniaxial tensile test. Additionally, two experimental test-benches are implemented with the aim to quantify the accuracy and the quality of event detection of the presented method. Finally, the obtained results are compared with four different techniques, representing the current state of the art related to AE activity detection.

中文翻译：

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一种基于短时波形特征的声发射活动检测方法：在单轴拉伸试验中应用于金属部件

摘要 声发射 (AE) 现象已被用作一种强大的工具，其目的是检测、定位或评估各种应用的损坏。由于其监测，当前对采集信号进行分析的一个主要挑战是正确识别和分离每个 AE 事件。当前检测事件的先进方法主要集中在高精度识别 AE 波的起点；然而，文献中忽略了对结论的检测。对于数据流内事件的自动连续检测，事件结论的这种缺乏准确性在两个关键方面产生错误。一方面，它降低了事件持续时间测量的准确性，截断了事件的跨度，这在评估应用程序中是不可取的；另一方面，它会导致错误检测。在这项工作中，使用受语音处理领域启发的框架，提出了一种准确且计算效率高的 AE 活动检测器，该框架提供了准确检测 AE 事件开始和结束所需的指标。这是通过阈值方法实现的，该方法不是直接使用换能电压信号进行操作，而是通过信号的短期能量和短期过零率测量来实现。STE-ZCR 方法是为与连续监测单个 AE 通道相关的应用而开发的，该通道通过单轴拉伸试验从金属部件的表征中获得。此外，实施了两个实验测试平台，目的是量化所提出方法的事件检测的准确性和质量。最后，将获得的结果与四种不同的技术进行比较，代表与 AE 活动检测相关的当前技术水平。